KiDS-1000 Cosmology: machine learning -- accelerated constraints on Interacting Dark Energy with COSMOPOWER

Understand

We derive constraints on a coupled quintessence model with pure momentum exchange from the public $\sim$1000 deg$^2$ cosmic shear measurements from the Kilo-Degree Survey and the $\it{Planck}$ 2018 Cosmic Microwave Background data.

• We compare this model with $\Lambda$CDM and find similar $\chi^2$ and log-evidence values.
• We accelerate parameter estimation by sourcing cosmological power spectra from the neural network emulator COSMOPOWER.
• We highlight the necessity of such emulator-based approaches to reduce the computational runtime of future similar analyses, particularly from Stage IV surveys.